Volume 12, Issue 1 (Journal of Control, V.12, N.1 Spring 2018)                   JoC 2018, 12(1): 39-52 | Back to browse issues page


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Abadianzadeh F, Derhami V, Rezaeain M. Designing a Fuzzy Controller for Visual Servoing of a Robot Manipulator with Online Adjustment Capability. JoC. 2018; 12 (1) :39-52
URL: http://joc.kntu.ac.ir/article-1-415-en.html
1- Yazd University
Abstract:   (9751 Views)

Vision-based robot control is a method to motion control of a robot using information extracted from visual sensors. In traditional approaches, a model of robot and camera are needed. Obtaining these models are time consuming and sometimes impossible. Recently, intelligent methods are used to cope the above challenges. In this paper, a hybrid fuzzy controller is proposed to control a robot manipulator. Visual inputs of the controller are provided by Kinect and outputs are the rotation of joints motors. The hybrid controller contains two controllers. The first controller in based on fuzzy inverse model which approximates real inverse model of robot using gathered data. In order to increase accuracy, a fuzzy expert controller is designed and it is used when the end-effector is in the predefined near-goal area. Since determining exact value of the fuzzy expert controller parameters is impossible, in addition to make system adaptive with small changes in the environment, actor-critic architecture is used. This architecture is a well known continuous reinforcement learning methods. The proposed method is applied to control a real robot manipulator (ARM_6AX18). Experimental results show that using the proposed method in practice, the end-effector reaches from any random start position to the goal position with a good accuracy in robot workspace.

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Type of Article: Review paper | Subject: Special
Received: 2016/10/21 | Accepted: 2017/10/10 | Published: 2018/04/5

References
1. [1] D. Kragic, and H. Christensen, "Survey on Visual Servoing for Manipulation," Computational Vision and Active Perception Laboratory, Fiskartorpsv 15 ,2002.
2. [2] F. Nadi, "Visual Servoing Control of Robot Manipulator with Jacobian Matrix Estimation, " (in Persian) M.S. Thesis, Faculty of Electrical and Computer Engineering, Yazd University 2014.
3. [3] P. Goncalves, L. Mendonca, J. Sousaand, and J. Pinto, "Uncalibrated Eye-to-Hand Visual Servoing Using Inverse Fuzzy Models," IEEE Transactions on Fuzzy Systems, pp. 341–353, 2008.
4. [4] C. Distante, A. Anglani, and F. Taurisano, "Target Reaching by Using Visual Information and Q-learning Controllers," Autonomous Robots, vol. 9, pp. 41–50, 2000.
5. [5] A. Anglani, F. Taurisano, R. De Giuseppe, C. Distante, and L. Lecce, "Learning to Grasp by Using Visual Information Robot System and Controller Architecture," Autonomous Robots, vol. 9, pp. 41–50, 2000.
6. [6] M. Sadeghzadeh, "Self-Learning Visual Servoing of Robot Manipulator Using Explanation-Based Fuzzy Neural Networks and Q-Learning," Ph.D. Dissertation, University of Guelph, 2014.
7. [7] K. Shibata, M. Sugisaka, and K. Ito, "Hand Reaching Movement Acquired Through Reinforcement Learning," in Proceedings of 2000 KACC (Korea Automatic Control Conference), 2000.
8. [8] Z. Miljkovic, M. Mitic, M. Lazarevic, and B. Babic, "Neural Network Reinforcement Learning for Visual Control of Robot Manipulators," Expert Systems with Applications, vol. 40, pp. 1721–1736, 2013.
9. [9] M. Deisenroth, C. Rasmussen, and D. Fox, "Learning to Control a Low-Cost Manipulator Using Data-Efficient Reinforcement Learning," International Conference on Robotics: Science & Systems, pp. 57–64, 2011.
10. [10] Robotic Arms. (n.d.). Pishrorobot. [Online]. Available:http://www.pishrobot.com/en/products/robotic_arms.htm. Accessed 19 Aug 2016.
11. [11] Kinect | Xbox 360. Xbox.com. (n.d.). [Online]. Available: http://www.xbox.com/en-US/xbox-360/accessories/kinect. Accessed 19 Aug 2016.
12. [12] J. Jang, C. Sun, and E. Mizutani, Neuro-fuzzy and Soft Computing. Upper Saddle River, NJ: Prentice Hall, 1997.
13. [13] S. Guillaume. "Designing Fuzzy Inference Systems from Data: An Interpretability-Oriented Review." IEEE Transactions on fuzzy systems, pp. 426-443, 2001.
14. [14] R. S. Sutton and A. G. Barto, "Introduction to Reinforcement Learning," IEEE Transactions on Robotics and Automation, MIT Press, 1998.
15. [15] L. P. Kaelbling, M. L. Littman, and A. W. Moore, "Reinforcement Learning: A survey," Journal of Artificial Intelligence Research, vol. 1, no. 1, pp. 237– 285, 1996.
16. [16] R. Sutton, and A. G. Barto, "Reinforcement learning," Journal of Cognitive Neuroscience, vol. 11, no. 1, pp. 126-134, 1999.
17. [17] X. S. Wang, Y. H. Cheng, and J. Q. Yi "A Fuzzy Actor–Critic Reinforcement Learning Network," Journal of Information Sciences, vol. 177, pp. 3764– 3781, 2007.
18. [18] L. X. Wang, (1997): A course in fuzzy systems and control. 1. Aufl. Upper Saddle River, NJ: Prentice Hall PTR.
19. [19] R. Babuska, Fuzzy Modeling for Control. Boston, MA: Kluwer Academic, 1998.
20. [20] FJ. Chang, and YT. Chang. "Adaptive Neuro-Fuzzy Inference System for Prediction of Water Level in Reservoir." Advances in Water Resources. pp. 1-10, 2006.
21. [21] Fuzzy hybrid control of robot with camera, aparat, 2016. [Online]. Available: http://www.aparat.com/v/5UWeh. Accessed: 08 Jul 2016.
22. [22] Fuzzy adaptive control of robot with camera, aparat, 2016. [Online]. Available: http://www.aparat.com/v/blqN0. Accessed: 17 Oct 2016.

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